Multi-objective evolutionary algorithm with non-stationary search space

2002 ◽  
Author(s):  
E.F. Khor ◽  
K.C. Tan ◽  
T.H. Lee
Keyword(s):  
Evolutionary Algorithm ◽  
Search Space ◽  
Multi Objective

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Localization for Solving Noisy Multi-Objective Optimization Problems

2009 ◽  
Vol 17 (3) ◽  
pp. 379-409 ◽  
Author(s):  
Lam T. Bui ◽  
Hussein A. Abbass ◽  
Daryl Essam
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Local Models ◽  
Multi Objective Optimization ◽  
Global Models ◽  
Average Performance ◽  
Multi Objective ◽  
Wide Range ◽  
Filter Noise

This paper investigates the use of a framework of local models in the context of noisy evolutionary multi-objective optimization. Within this framework, the search space is explicitly divided into several nonoverlapping hyperspheres. A direction of improvement, which is related to the average performance of the spheres, is used for moving solutions within each sphere. This helps the local models to filter noise and increase the robustness of the evolutionary algorithm in the presence of noise. A wide range of noisy problems we used for testing and the experimental results demonstrate the ability of local models to better filter noise in comparison with that of global models.

Multi-objective evolutionary algorithm based on neighborhood

2008 ◽  
Vol 28 (6) ◽  
pp. 1570-1574
Author(s):  
Mi-qing LI ◽  
Jin-hua ZHENG ◽  
Biao LUO ◽  
Jun WU ◽  
Shi-hua WEN
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective
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